RLHF Simulator

AI models aren't just trained on text from the internet — they're fine-tuned by humans. In Reinforcement Learning from Human Feedback (RLHF), human trainers compare pairs of AI responses and pick the better one. Those preferences train a Reward Model that learns what "good" looks like. Over millions of comparisons, this shapes the AI from a raw text predictor into something that feels genuinely helpful. Below, you're the trainer.
Choose a Scenario
Training progress
0 / 3 rated
Prompt sent to AI
Which response is better? Click to choose.
Response A
A
Reward score
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Response B
B
Reward score
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Why This Matters
Without RLHF, a language model trained only on internet text would be a raw prediction engine — technically impressive but often unhelpful, evasive, or weirdly literal. Human feedback teaches it that a clear, empathetic answer beats a technically correct but cold one; that admitting uncertainty is better than guessing; that tone matters as much as content. The reward model built from your ratings becomes an invisible referee that steers every future response. The chatbot you use every day is the result of millions of decisions like the ones you just made.